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The paper presents an approach to investigate and monitor the air pollution caused by the palm oil mill. A concept of dealing with the problem from its causes is used where the sources of pollution from the stack gases were examined. The main causes were from the combustion of shell fibre and of the palm oil. However, in the boiler itself, several parameters like steam load and pressure, fuel capacity and temperature also contribute to the pollution. The study uses Neural Network (NN) to simulate the process of combustion and stack gases. This neural network was trained by using the data on emission and combustion bed taken from local palm oil plant in Perak, Malaysia. The trained data by NN agrees well with the measured data, i.e. almost within 8% error for pollutants like CO, SO2, NOx and particulate matters.

APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN PREDICTING PALM OIL MILL EMISSION